Astrophysical image denoising using bivariate isotropic cauchy distributions in the undecimated wavelet domain
Achim A., Herranz D., Kuruoglu E. E.
Wavelet transform Alpha-stable distributions Bivariate models
Within the framework of wavelet analysis, we describe a novel technique for removing noise from astrophysical im- ages. We design a Bayesian estimator, which relies on a particular member of the family of isotropic ®-stable dis- tributions, namely the bivariate Cauchy density. Using the bivariate Cauchy model we develop a noise-removal pro- cessor that takes into account the interscale dependencies of wavelet coe±cients. We show through simulations that our proposed technique outperforms existing methods both visually and in terms of root mean squared error.
Source: IEEE International Conference on Image Processing (ICIP), pp. 1225–1228, Singapore, 24-27 October 2004
Publisher: IEEE Computer Society, Washington, DC, USA
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@inproceedings{oai:it.cnr:prodotti:91054,
title = {Astrophysical image denoising using bivariate isotropic cauchy distributions in the undecimated wavelet domain},
author = {Achim A. and Herranz D. and Kuruoglu E. E.},
publisher = {IEEE Computer Society, Washington, DC, USA},
booktitle = {IEEE International Conference on Image Processing (ICIP), pp. 1225–1228, Singapore, 24-27 October 2004},
year = {2004}
}
0000-0002-2608-8034